0 viewsTalent
Diana K. — Junior Applied Mathematics Researcher from France

Diana K.

Junior Applied Mathematics Researcher

France No experience yet
Open to offersNew to Platform
Languages
EnglishFrenchArabic
Video Introduction
No video introduction yet
The candidate has not added a video.
Contact information and social networks are private. Connect to unlock.
Hidden

About

Diana K., holding a PhD in Mathematical Computer Science from CEA Saclay and Paris Saclay University, is an expert in applied mathematics with a focus on non-negative matrix factorization, signal processing, and scientific software development. She developed DIANMF, an innovative open-source R package, that enhances the analysis of metabolomics datasets through the blind unmixing of SWATH-DIA mass spectrometry data. Her work, which integrates advanced methods in signal processing and machine learning, has been validated extensively, demonstrating improved metabolite identification. Diana possesses strong foundational knowledge in real and complex analysis, differential equations, and spectral analysis. She has honed her programming skills in R, Python, and MATLAB, while contributing to key publications in the field of data science and robust-safety notions in differential inclusions. Diana is seeking to leverage her depth of experience in scientific software development within postdoctoral or industry R&D roles. Based in Paris, France, she is poised to make significant contributions to the intersection of applied mathematics and biomedical data analysis.

Experience

  • Doctoral Researcher

    CEA Saclay · 2022 — 2026
    Created DIANMF, an open-source R package for blind source separation of high-resolution LC-MS/MS SWATH-DIA data. Developed a sparse-NMF framework using Non-negative Generalized Morphological Component Analysis with FISTA acceleration. Innovated preprocessing and post-processing techniques for MS1/MS2 signal extraction and unmixing across SWATH isolation windows. Conducted thorough validation on various datasets, demonstrating improved compound identification.
  • Research Intern

    Gipsa Lab · 2022 — 2022
    Formulated the strong robust-safety concept for differential inclusions, addressing control loops affected by perturbations. Established mathematical models to evaluate inner and outer perturbations, proving that strong robust safety surpasses classical notions. Developed necessary conditions for strong robust safety using barrier functions, ensuring a comprehensive theoretical framework.

Skills & Expertise

Education

  • PhD in Mathematical Computer Science
    CEA Saclay and Paris Saclay University · — — 2026
  • Master's 2 in Applied Mathematics
    Limoges University · 2021 — 2022
  • Bachelor's degree in Computer Science-Informatics
    Lebanese University
  • Master's degree in Mathematics
    Lebanese University

Interested in this professional?

Sign in as an employer to save this profile or invite Diana K. to a job.

Sign in as an employer